In my previous post I discussed how an organization should leverage data quality scorecards within their environment.  The purpose of this was two-fold, as it provided a starting point for ensuring collaboration between the business and IT groups around the data and also provided a snapshot into how an organization is doing today with identifying and resolving identified issues. 

Although generating data quality scorecards sounds easy enough, the underlying problem is often deeper than just a data issue; rather, it is an organizational issue around developing a framework that ensures frequent collaboration between business and IT.  I call this collaboration the “Holy Grail of Data Quality.” Without collaboration, any tools and processes currently in place are destined for failure, as the people aspect (the third pillar for success in a data quality initiative) is missing.  In addition, building and then maintaining collaboration can be the most difficult part of ensuring a successful and sustainable data quality initiative.

In a recent post, Andrew White from Garter commented that “Business users tend, if you are luck, to accept that they “own” process, but few accept that they do, or should, own data.”  I agree with this comment, as I think the focus should be on the stewardship of the data, versus the topic of ownership.  The reason is that the question of ownership often muddies the water.  For example, IT might own the system or the application, whereas Finance might be responsible for usage of the data in the application.  Rather than focus on who owns the data, my conversations with organizations focus begin with who are the stewards of data – both from an IT and business standpoint.  Through this shift in terminology, the intent is to begin to break down any barriers that may exist between the business and their IT counterparts as we look for a starting point for collaboration. 

Where does Technology Fit?

Although technology can’t solve organizational or the cultural changes that are required to build out a data quality initiative, they can help expedite and facilitate the process.  The key is finding tools that will help improve existing data management processes without bringing a new level of complexity to the organization. 

Software such as SAP BusinessObject’s recently announced Information Steward are designed to help bring a level of transparency to the data analysis process, which in turn, allows for business to analyze and share the results of the data issues with their IT counterparts.  This can be in the form of scorecards as well as the development of cleansing packages that can be used in the data integration processes for the ongoing data quality management efforts.

If technology currently exists within your organization for data profiling and data quality, start to understand how it is currently leveraged within the company.  Quite often, enterprise class software is being used for a departmental need (in a silo) versus being something that provides repeatable processes that can be the foundation for an enterprise wide data quality strategy.

Where Do I Start?

When working with organizations, I often recommend that a Readiness Assessment engagement serves as a starting point.  The purpose of the assessment is to take an inventory of the current state of the data, the people (stewards) that will be a part of a data quality initiative and any processes currently in place for data quality.  Once we know what we are starting with, we collectively develop a plan to start small (versus the “boil the ocean” approach), as we want to see achievable results in a short period of time.  These results should help build initial momentum which ideally results in the approval to pursue additional data quality efforts.  At the same time, we are building a foundation for ensuring sustained and ongoing momentum around a data quality initiative.  

How Does Your Organization Rate?

The question I would like to pose is – why do you think collaboration breaks down within your organization?  If the reasons are known, what changes related to people, processes and/or technology need to take place to fix it?